The possibility to determine the position of a mobile device has enabled application developers and wireless network operators to provide location based, and location aware, services. Examples of those are guiding systems, shopping assistance, friend finder, presence services, community and communication services and other information services giving the mobile user information about their surroundings.
In addition to the commercial services, the governments in several countries have put requirements on the network operators to be able to determine the position of an emergency call. For instance, the governmental requirements in the USA (FCC E911) state that it must be possible to determine the position of a certain percentage of all emergency calls. The requirements make no difference between indoor and outdoor environment.
In outdoor environments, the position estimation can be done using e.g. the global positioning system, i.e. GPS (Global Positioning System), or methods based thereon, such as Assisted-GPS (A-GPS). However, this requires that the user equipment has to be provided with additional functionalities concerning e.g. reception of radio signals.
Position estimation can also be performed using the wireless network itself. Methods using the wireless network can be grouped in different groups. A first group comprises methods that are based on the identification of the radio cell to which a mobile terminal is attached, e.g. by using Cell-ID. In its simplest form, a user equipment (UE) is known to be situated within the coverage area of a certain base station if the user equipment is communicating with the wireless network through that base station. This can be improved by also taking information from so-called neighbor lists into account. However, the accuracy is even then not very impressive.
Another group of position estimation methods are based on measurements of signal propagation times or quantities related thereto. Timing Advance (TA) in LTE systems and Round Trip Time (RTT) in WCDMA systems are examples of such methods. Briefly, the travel time of radio waves from the Radio Base Station (RBS) to the UE and back is measured. The round trip time measurement alone defines a circle, or if the inaccuracy is accounted for, a circular strip around the RBS, within which the UE is located. By combining such information with propagation times to neighboring RBS's enabling trilatheration calculations, the accuracy can be improved somewhat. However, this possibility does only occur in a limited part of the cells, typically less than 25%. The signal propagation time measurements can also be combined with Cell-ID information, which typically restricts the area in which the UE can be situated to the sector of the circular strip being situated within the cell. As for other terrestrial positioning methods, like observed time difference of arrival (OTDOA), these suffer from a too low detection performance to provide good enough performance, at least in the basic configuration.
A more promising approach is provided by so called fingerprinting positioning, see e.g. “Adaptive enhanced cell-ID fingerprinting localization by clustering of precise position measurement”, in IEEE Trans. Vehicular Tech., vol. 56, no. 5, 2007, pp. 3199-3209 by T. Wigren. Fingerprinting positioning algorithms operate by creating a radio fingerprint for each point of a fine coordinate grid that covers the Radio Access Network (RAN). The fingerprint may e.g. consist of detectable cell ID's, quantized path loss or signal strength measurements, quantized RTT or TA, quantized noise rise, radio connection information like the radio access bearer (RAB) and/or quantized time.
When providing position determination assisting data in a cellular communications network necessary for adaptive enhanced cell ID (AECID) positioning, a cell relation configuration is established for a user equipment, for which a tag is created, and a high-precision position determination is performed for the same user equipment. This is repeated a plurality of times. A second step is to collect all high precision positioning measurements that have the same tag in separate high precision measurement clusters. In a third step, a polygon that represents the geographical extension of a cluster is computed, for each stored high precision position measurement cluster. The details of this algorithm are disclosed in the published international patent applications WO 2007/043915, WO 2008/118052 and WO 2008/069712. The area of the polygon is typically minimized, which in turn maximizes the accuracy when used. The probability that the terminal is within the polygon, i.e. the confidence, is precisely known as it is set as a constraint in the algorithm. The typical approach to this is to create a polygon that is known to enclose all clustered measurements of the tag to be treated. A contraction point is selected within that polygon and the polygon is shrunk according to different algorithms towards that contraction point, under the constraint that a certain fraction of all clustered measurements are maintained within the polygon, until a minimum area of the polygon is obtained.
When a positioning is to be performed, a fingerprint is detected and compared with stored relations between fingerprints and position. In such a way, an area within which the UE with a certain certainty is situated can be achieved.
However, it has been found that the present routines for the fingerprinting technology do not operate optimally in certain cases. It has been found that the polygon shrinking procedures in some occasions find local minima of the area instead of a more global minimum.
There is hence a need for methods and apparatuses further improving the positioning accuracies by finding improved polygon shrinking routines or alternatives to the polygon shrinking routines.